Importance

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Abstract

Improved facial recognition tracking of individuals throughout a space is provided, as is identification of unexpected behavior. The system can configure itself upon setup and adjust to changing conditions, and is able to intelligently reduce the workload on the facial recognition system. Cameras are placed throughout a building and learn what typical traffic within the building looks like. Over time, the system can track multiple users throughout the system and can automatically learn the average time between cameras. A probability function for each camera can also be determined that give probabilities for each camera to camera path. This approach provides for both limiting the bandwidth and processing power required for facial recognition and also allows for behavioral analysis. This system could be implemented as a distributed system of cameras, each performing its own facial recognition and tracking, and/or with distributed cameras combined with central processing for facial recognition.

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First Claim